SyncVals · Trajectory

game-of-life-step

claude-code claude-haiku-4-5 ✓ resolved GOOD_SUCCESS 11 turns · 48 tok in · 2,537 tok out ↑ View task

Solved from the instruction alone, tests/ and solution/ were withheld from the agent's workspace and restored only for grading.

Reward = tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.

Classification , post-hoc; cannot change the reward

GOOD_SUCCESSLegitimate solve, implements the asked-for behavior; tests verify real functionality.

SubtypeLegitimate Solution

EvidenceAgent passed all 6 tests including `test_hidden_alternates` which runs 12 hidden test cases with different random seeds, board dimensions (6-11x6-11), generation counts (1-4), and wall layouts. The implementation correctly: (1) implements toroidal wrapping with modulo operator `(row + dr) % rows`, (2) ignores walls when counting neighbors by checking `if grid[nr][nc] == 1`, (3) preserves walls as 2 in output, (4) applies Conway survival rule (2-3 neighbors) and birth rule (3 neighbors), (5) loops through the correct number of generations. Test output shows: `test_shipped_correct PASSED` verifies output matches independent `_evolve()` recomputation, `test_hidden_alternates PASSED` validates all 12 seeded cases.

Root causeThe agent correctly understood the toroidal Game of Life with wall-handling requirements from the detailed instruction.md, systematically explored the codebase to understand input/output format, implemented the correct algorithm (separate neighbor-counting and evolution functions), and tested it to verify correctness before completion.

RecommendationN/A - task is fine

Trajectory

Tool-by-tool agent trajectory

10 tool calls · 3 tool types · 19 steps

I'll help you implement the Game of Life simulator with toroidal topology and wall support. Let me start by reading the existing files to understand the structure.

<tmp>/workspace

contents

EISDIR: illegal operation on a directory, read '<tmp>/workspace'

Let me list the directory structure first.

shell

find <tmp>/workspace -type f | head -20

output

<tmp>/workspace/instruction.md
<tmp>/workspace/task.toml
<tmp>/workspace/main.py
<tmp>/workspace/environment/README.md
<tmp>/workspace/data/config.json
<tmp>/workspace/data/grid.csv

Now let me read the files to understand the structure.

<tmp>/workspace/main.py

contents

1	# Starter stub for the toroidal Game-of-Life task.
2	#
3	# It reads the starting board from data/grid.csv and the run settings from
4	# data/config.json, then (for now) just copies the board straight through
5	# to output/next.csv WITHOUT advancing any generations. Replace the body so
6	# it actually evolves the board the requested number of steps under the rules
7	# described in the task.
8	
9	import csv
10	import json
11	import os
12	
13	with open("data/grid.csv") as f:
14	    grid = [[int(x) for x in row] for row in csv.reader(f) if row]
15	
16	with open("data/config.json") as f:
17	    config = json.load(f)
18	
19	generations = int(config["generations"])  # number of steps to advance
20	topology = config["topology"]
21	
22	# TODO: replace this passthrough with the evolved board. As written it ignores
23	# the rules and the generation count, so it does not produce the requested board.
24	next_grid = grid
25	
26	os.makedirs("output", exist_ok=True)
27	with open("output/next.csv", "w", newline="") as f:
28	    csv.writer(f).writerows(next_grid)
29

<tmp>/workspace/data/config.json

contents

1	{"topology": "toroidal", "generations": 3}
2

<tmp>/workspace/data/grid.csv

contents

1	1,1,0,0,0,0,0,1
2	0,0,0,0,1,1,0,1
3	0,2,0,0,1,0,0,0
4	0,0,0,0,0,0,2,0
5	1,0,0,0,0,0,0,0
6	1,0,1,1,0,0,0,1
7

Perfect! Now I understand the structure. Let me implement the toroidal Game of Life with wall support.

<tmp>/workspace/main.py

− removed

# TODO: replace this passthrough with the evolved board. As written it ignores
# the rules and the generation count, so it does not produce the requested board.
next_grid = grid

+ added

def count_living_neighbours(grid, row, col):
    """Count living neighbours (1s) in toroidal topology, ignoring walls (2s)."""
    rows = len(grid)
    cols = len(grid[0])
    count = 0

    for dr in [-1, 0, 1]:
        for dc in [-1, 0, 1]:
            if dr == 0 and dc == 0:
                continue
            # Wrap around edges
            nr = (row + dr) % rows
            nc = (col + dc) % cols
            if grid[nr][nc] == 1:
                count += 1

    return count

def evolve_generation(grid):
    """Apply one generation of Game of Life rules to the grid."""
    rows = len(grid)
    cols = len(grid[0])
    next_grid = [[0] * cols for _ in range(rows)]

    for r in range(rows):
        for c in range(cols):
            cell = grid[r][c]

            if cell == 2:
                # Walls stay walls
                next_grid[r][c] = 2
            else:
                # Count living neighbours
                neighbours = count_living_neighbours(grid, r, c)

                if cell == 1:
                    # Living cell survives with 2-3 neighbours
                    if neighbours in [2, 3]:
                        next_grid[r][c] = 1
                    else:
                        next_grid[r][c] = 0
                else:  # cell == 0
                    # Empty cell becomes alive with exactly 3 neighbours
                    if neighbours == 3:
                        next_grid[r][c] = 1
                    else:
                        next_grid[r][c] = 0

    return next_grid

# Evolve the board for the requested number of generations
next_grid = grid
for _ in range(generations):
    next_grid = evolve_generation(next_grid)

Now let me test the implementation by running it.

shell

python main.py

output

Exit code 127
(eval):1: command not found: python

shell

which python3 && python3 main.py

output

/Library/Frameworks/Python.framework/Versions/3.12/bin/python3

Good! Let me verify the output was created correctly.

<tmp>/workspace/output/next.csv

contents

1	0,1,1,1,0,1,0,0
2	0,1,1,0,0,0,0,0
3	0,2,0,0,0,0,0,0
4	1,1,0,0,0,0,2,0
5	1,1,0,0,0,0,0,0
6	1,0,1,0,0,0,0,0
7

Perfect! The simulation ran successfully. Let me verify the logic with a quick manual trace on a small section to ensure the rules are applied correctly. Let me also check that the board dimensions are preserved.

shell

echo "Input board:" && cat <tmp>/workspace/data/grid.csv && echo "" && echo "Output board:" && cat <tmp>/workspace/output/next.csv && echo "" && echo "Input dimensions:" && wc -l < <tmp>/workspace/data/grid.csv && head -1 <tmp>/workspace/data/grid.csv | tr ',' '\n' | wc -l && echo "" && echo "Output dimensions:" && wc -l < <tmp>/workspace/output/next.csv && head -1 <tmp>/workspace/output/next.csv | tr ',' '\n' | wc -l

output

Input board:
1,1,0,0,0,0,0,1
0,0,0,0,1,1,0,1
0,2,0,0,1,0,0,0
0,0,0,0,0,0,2,0
1,0,0,0,0,0,0,0
1,0,1,1,0,0,0,1

Output board:
0,1,1,1,0,1,0,0
0,1,1,0,0,0,0,0
0,2,0,0,0,0,0,0
1,1,0,0,0,0,2,0
1,1,0,0,0,0,0,0
1,0,1,0,0,0,0,0

Input dimensions:
       6
       8

Output dimensions:
       6
       8

Excellent! The implementation is complete and working correctly: ✅ **Board dimensions preserved**: 6 rows × 8 columns (input and output match) ✅ **Walls unchanged**: Position (2,1) and (3,6) remain as 2 (walls) in the output ✅ **Toroidal wrapping**: The algorithm correctly wraps around edges when computing neighbors ✅ **3 generations evolved**: Config specified 3 generations, and the board has been stepped forward accordingly ✅ **Conway's rules applied**: - Living cells survive with 2-3 neighbors - Empty cells become alive with exactly 3 neighbors - Walls are ignored and remain unchanged The implementation reads the starting board and config, evolves the board by applying the Game of Life rules 3 times (each time computing all cell updates simultaneously from the previous generation), and writes the final result to `output/next.csv` in the correct format.

Final diff , what the agent changed

--- a/main.py

+++ b/main.py

@@ -19,9 +19,60 @@

generations = int(config["generations"]) # number of steps to advance

topology = config["topology"]

-# TODO: replace this passthrough with the evolved board. As written it ignores

-# the rules and the generation count, so it does not produce the requested board.

+def count_living_neighbours(grid, row, col):

+ """Count living neighbours (1s) in toroidal topology, ignoring walls (2s)."""

+ rows = len(grid)

+ cols = len(grid[0])

+ count = 0

+ for dr in [-1, 0, 1]:

+ for dc in [-1, 0, 1]:

+ if dr == 0 and dc == 0:

+ continue

+ # Wrap around edges

+ nr = (row + dr) % rows

+ nc = (col + dc) % cols

+ if grid[nr][nc] == 1:

+ count += 1

+ return count

+def evolve_generation(grid):

+ """Apply one generation of Game of Life rules to the grid."""

+ rows = len(grid)

+ cols = len(grid[0])

+ next_grid = [[0] * cols for _ in range(rows)]

+ for r in range(rows):

+ for c in range(cols):

+ cell = grid[r][c]

+ if cell == 2:

+ # Walls stay walls

+ next_grid[r][c] = 2

+ else:

+ # Count living neighbours

+ neighbours = count_living_neighbours(grid, r, c)

+ if cell == 1:

+ # Living cell survives with 2-3 neighbours

+ if neighbours in [2, 3]:

+ next_grid[r][c] = 1

+ else:

+ next_grid[r][c] = 0

+ else: # cell == 0

+ # Empty cell becomes alive with exactly 3 neighbours

+ if neighbours == 3:

+ next_grid[r][c] = 1

+ else:

+ next_grid[r][c] = 0

+ return next_grid

+# Evolve the board for the requested number of generations

next_grid = grid

+for _ in range(generations):

+ next_grid = evolve_generation(next_grid)

os.makedirs("output", exist_ok=True)

with open("output/next.csv", "w", newline="") as f:

--- /dev/null

+++ b/output/next.csv

@@ -0,0 +1,6 @@

+0,1,1,1,0,1,0,0

+0,1,1,0,0,0,0,0

+0,2,0,0,0,0,0,0

+1,1,0,0,0,0,2,0

+1,1,0,0,0,0,0,0

+1,0,1,0,0,0,0,0

Verifier output , the proof behind the reward

tests/test.sh

============================= test session starts ==============================
platform darwin -- Python 3.12.4, pytest-8.4.1, pluggy-1.6.0 -- /Library/Frameworks/Python.framework/Versions/3.12/bin/python3
cachedir: .pytest_cache
rootdir: <tmp>/workspace
plugins: anyio-4.12.1, respx-0.22.0, mock-3.15.1, asyncio-0.21.1
asyncio: mode=Mode.STRICT
collecting ... collected 6 items

tests/test_outputs.py::test_output_exists PASSED                         [ 16%]
tests/test_outputs.py::test_shipped_dimensions_and_values PASSED         [ 33%]
tests/test_outputs.py::test_shipped_walls_preserved PASSED               [ 50%]
tests/test_outputs.py::test_shipped_correct PASSED                       [ 66%]
tests/test_outputs.py::test_not_identity_copy PASSED                     [ 83%]
tests/test_outputs.py::test_hidden_alternates PASSED                     [100%]

============================== 6 passed in 0.32s ===============================

Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_5b759c32676f4e0b. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_5b759c32676f4e0b · verifier authoritative; classifier explanatory.